| 基于A-Train综合资料的云顶高度反演研究 |
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| 引用本文： | 李冠林,严卫,王蕊,韩丁,陆文,万应虎,里巍.基于A-Train综合资料的云顶高度反演研究[J].气象,2016,42(8):971-979. |
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| 作者姓名： | 李冠林 严卫 王蕊 韩丁 陆文 万应虎 里巍 |
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| 作者单位： | 解放军理工大学气象海洋学院， 南京 211101,解放军理工大学气象海洋学院， 南京 211101,解放军理工大学气象海洋学院， 南京 211101,解放军理工大学气象海洋学院， 南京 211101,解放军理工大学气象海洋学院， 南京 211101,解放军理工大学气象海洋学院， 南京 211101,解放军理工大学气象海洋学院， 南京 211101 |
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| 基金项目： | 国家自然科学基金青年科学基金项目（41306187）资助 |
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| 摘 要： | 提出一种适用性较强的云顶高度反演方法。利用2007年低纬地区(15°S~15°N)的A-Train综合资料反演云顶高度。首先以M0DIS通道31和通道32的亮温值为特征参数,基于SVM分类法,将云分为不透明云、半透明云和透明云三类,分类准确率达到90.6%。然后对三类云分别用核回归法反演云顶高度,将其与CloudSat的2B-GEOPROF-LIDAR产品对比,均方根误差分别为0.95、1.17和1.27 km。与未分类的核回归法结果相比,分类后三种云的反演误差都有所减小。最后分析了三个典型个例。该方法可推广至其他含有红外分裂窗通道的卫星上,发挥更多卫星资源的效用。
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| 关 键 词： | 云顶高度，红外分裂窗，A Train，支持向量机，核回归 |
| 收稿时间： | 2015/8/12 0:00:00 |
| 修稿时间： | 6/3/2016 12:00:00 AM |
Research on Retrieval of Cloud Top Height Based on A-Train Data |
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| LI Guanlin,YAN Wei,WANG Rui,HAN Ding,LU Wen,WAN Yinghu and LI Wei.Research on Retrieval of Cloud Top Height Based on A-Train Data[J].Meteorological Monthly,2016,42(8):971-979. |
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| Authors: | LI Guanlin YAN Wei WANG Rui HAN Ding LU Wen WAN Yinghu and LI Wei |
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| Institution: | College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101,College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101,College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101,College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101,College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101,College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 and College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 |
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| Abstract: | An applicable method to retrieve cloud top height (CTH) is proposed. Using the A Train data in 2007, CTH in low latitude area (15°S~15°N) is retrieved. Firstly, using the brightness temperatures at Channels 31 and 32 of MODIS, clouds are classified into three categories including opaque clouds, semi transparent clouds and transparent clouds based on SVM method with 90.6% accuracy. Then, the CTH of the three categories are retrieved by kernel regression.The root mean square errors (RMSE) are 0.95 km, 1.17 km and 1.27 km, respectively, compared with CloudSat 2B GEOPROF LIDAR product.The errors of the three categories of clouds all decrease after being classified. Lastly, three typical cases are analyzed. The algorithm could be carried out in other satellites containing infrared split window channels. |
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| Keywords: | cloud top height infrared split window A Train SVM kernel regression |
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